With growing interest in sustainable logistics, electric vehicle (EV)-based deliveries offer a promising alternative for urban distribution. However, EVs face challenges due to their limited battery capacity, requiring careful planning for recharging. This depends on factors such as the charging point (CP) availability, cost, proximity, and vehicles’ state of charge (SoC). We propose CARGO, a framework addressing the EV-based delivery route planning problem (EDRP), which jointly optimizes route planning and charging for deliveries within time windows. After proving the problem’s NP-hardness, we propose a mixed integer linear programming (MILP)-based exact solution and a computationally efficient heuristic method. Using real-world datasets, we evaluate our methods by comparing the heuristic to the MILP solution, and benchmarking it against baseline strategies, Earliest Deadline First (EDF) and Nearest Delivery First (NDF). The results show up to 39% and 22% reductions in the charging cost over EDF and NDF, respectively, while completing comparable deliveries.

Autonomous Vehicles (AV s) require substantial computational resources to perform operations that safely navigate vehicles in urban road networks. Resource-intensive operations are offloaded to roadside units (RSUs), acting as edge servers, to improve the responsiveness and reduce the energy consumed in execution. In this context, a cooperative execution involving the vehicular on-board units (OBUs) and the RSUs can act as a game changer. However, partial offloading is non-trivial and demands addressing the following research challenges. Firstly, the RSU's resources are limited, necessitating regulated resource assignments. Secondly, capturing distinctive vehicle parameters using a unified ranking scheme is imperative. Thirdly, an efficient partition strategy must consider the energy expended and adhere to the real-time operations' deadline needs. This paper proposes a partial offloading scheme, MOVE, catering to the above-mentioned challenges. A deferred acceptance algorithm (DAA) with preferences is proposed to address the first two challenges, whereas a novel energy-aware partitioning strategy resolves the final challenge. The performance of the proposed scheme is evaluated against baseline algorithms, and we observed a 54.04 % and 52.17 % reduction in offloading latency and energy.

Autonomous vehicles (AVs) execute compute-intensive control operations like adjusting speed and steering, causing significant energy dissipation and latency due to resource-limited onboard units (OBUs). Offloading these tasks to Roadside Units (RSUs) is a solution, but it faces challenges. First, the stringent latency requirements are impacted by the vehicle’s stochastic velocity. Second, allocating limited RSU resources to numerous vehicles within its coverage area is difficult. This paper proposes the MIME framework to address these issues. We use Discrete Fourier transform (DFT) that computes the average velocity over an aperiodic velocity signal extracted from a real-world dataset. For resource allocation, we model it as matching with externalities, using reactive preferences based on vehicle speed and location. We present an efficient, scalable, stable solution, showing a 24.61% and 11.2% reduction in offloading latency and energy for the inD dataset, and a 5.6% and 5.52% reduction for the SUMO dataset.

Urban Air Mobility (UAM) encompasses both piloted and autonomous aerial vehicles, spanning from small unmanned aerial vehicles (UAVs) like drones to passenger-carrying personal air vehicles (PAVs), to revolutionize smart transportation in congested urban areas. This emerging paradigm is anticipated to offer disruptive solutions to the mobility challenges in congested cities. In this context, a pivotal concern centers on the sustainability of transitioning to this mode of transportation, especially with the focus on incorporating clean technology into developing innovative solutions from the ground up. Recent studies highlight that a significant portion of the total energy consumption in UAM can be attributed to the flight operations of the aircraft. To address this challenge, this paper introduces a framework POSCA aimed at meeting the energy requirements of UAM flights. It delves into a complex and dynamic route-planning problem. It introduces a novel concept called the Phototropic Index, calculated by considering the traversal distance and solar coverage along the route. To solve the path planning problem, we propose two solutions, S-POSCA and D-POSCA, catering to static and dynamic setups. Simulation results confirm an average increase of 8.81% in static conditions and 10.64% in the dynamic condition for the cumulative Global Horizontal Irradiance (GHI) compared to the baseline approaches.

Resource-constrained Internet of Things (IoT) devices depend on remote Cloud/Fog Nodes (FNs) to execute deadline-sensitive services. Offloading computations of real-time services to a remote cloud server results in intolerable latency due to intermittent channels, higher transmission delays, and scarce spectrum resources. Therefore, offloading to nearby FNs is preferable; however, it introduces several significant issues: (i) allocation of limited FN resources, (ii) deadline constraint of heterogeneous services, and (iii) requirement of computationally inexpensive and scalable strategies. This article proposes a M-DAFTO model to tackle the abovementioned issues and generate a fair offloading plan in polynomial time. The offloading problem is modeled as a many-to-one matching game with maximum and minimum quotas at each FN. Because the deferred acceptance (DA) algorithm fails to operate with minimum quotas, we adopt a variant of the DA algorithm, a multistage deferred acceptance (MSDA) algorithm, to solve the offloading problem. The overall goal of M-DAFTO is to reduce the aggregate offloading delay with increased assignment of tasks to FNs. Extensive simulation and analysis confirm a 30.26% and a 93.53% reduction in offloading delay and outages (unassigned tasks) compared to the baselines.

Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model calledCoMCLOUDto select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines.

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications’ power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is$\mathcal {NP}$-Hard, we propose anAnt Colony Optimization(ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by 25%–30% and electricity cost by approximately 25% compared to the baseline.

A virtual data center (VDC) comprises multiple virtual machines (VMs) with communication dependencies represented as virtual links (VLs). These virtual components, i.e., VMs and VLs, often experience fluctuating demands across different resource types. In this article, we focus on addressing the issue of dynamic resource expansion that leads to the relocation of solution components (SCs), where a SC comprises a VM and its attached VLs, with either the VM and/or at least one of the VLs facing resource expansion. This is challenging because of the complexity involved in frequently relocating multiple dependent virtual components across the substrate network. This article presents a model calledReMatchthat aims at building an efficient remapping plan with reduced remapping cost and improved resource utilization for service providers (SPs) in polynomial time. The overall relocation problem is formulated as a one-to-many matching game with heterogeneous VM demands. Owing to the inapplicability of the classical deferred acceptance algorithm (DAA) and revised DA (RDA), we propose a modified version of the RDA (MRDA) to obtain a weakly stable assignment. Thorough simulation and analysis show thatReMatchoutperforms the baseline algorithms considering multiple evaluation metrics.

The Internet of Things (IoT) devices are highly reliant on cloud systems to meet their storage and computational demands. However, due to the remote location of cloud servers, IoT devices often suffer from intermittent Wide Area Network (WAN) latency which makes execution of delay-critical IoT applications inconceivable. To overcome this, service providers (SPs) often deploy multiple fog nodes (FNs) at the network edge that helps in executing offloaded computations from IoT devices with improved user experience. As the FNs have limited resources, matching IoT services to FNs while ensuring minimum latency and energy from an end-user’s perspective and maximizing revenue and tasks meeting deadlines from a SP’s standpoint is challenging. Therefore in this paper, we propose a student project allocation (SPA) based efficient task offloading strategy called SPATO that takes into account key parameters from different stakeholders. Thorough simulation analysis shows that SPATO is able to reduce the offloading energy and latency respectively by 29% and 40% and improves the revenue by 25% with 99.3% tasks executing within their deadline.

The resource-constrained IoT devices often offload tasks to Fog nodes (FNs) owing to the intermittent WAN delays and multi-hopping by executing at remote cloud servers. An efficient allocation strategy satisfies the users' requirements by ensuring minimum offloading delays and provides a balanced assignment from the service providers' (SPs) viewpoint. This paper presents a model called LETO that reduces the total offloading delay for real-time tasks and achieves a balanced assignment across FNs. The overall problem is modeled as a one-to-many matching game with maximum and minimum quotas. Owing to the deferred acceptance algorithm (DAA) inapplicability, we use a proficient version of the DAA called multi-stage deferred acceptance algorithm (MSDA) to obtain a fair and Pareto-optimal assignment of tasks to FNs. Extensive simulations confirm that LETO can achieve a more balanced assignment compared to the baseline algorithms.

Typical cloud systems are often prone to inherent wide area network (WAN) latency. To address this issue fog computing is proposed that enables resource-constrained Internet-of-Things (IoT) devices, to execute deadline-sensitive tasks at the edge of the network. These devices can extend their battery lifespan by intelligently offloading computations as tasks to fog nodes (FNs) in their vicinity. However, finding an optimal offloading plan in a densely connected IoT-fog network is proven to beNP-Hard. Hence, in this article, we propose a matching theory-based efficient task offloading strategy called METO that aims to reduce the total system energy and number of outages (number of tasks exceeding the deadline) in an IoT-fog interconnection network. As resource allocation involves multiple criteria, their weights are derived using criteria importance though inter criteria correlation (CRITIC). Furthermore, to rank the alternatives we use the technique for order of preference by similarity to ideal solution (TOPSIS). Based on this ranking, we formulate the overall offloading problem as a one-to-many matching game and utilize the deferred acceptance algorithm (DAA) to produce a stable assignment. Simulation is performed in two different settings comprising offloading of homogeneous and heterogeneous tasks. Extensive simulations across both environments confirm that the proposed algorithm outperforms the existing schemes with respect to improved energy consumption, completion time, and execution time. Moreover, METO also shows the reduced number of outages across baselines used for comparison.

A virtual data center (VDC) mostly encapsulates multiple virtual machines (VMs) with communication dependencies. These VDC requests are dynamic in nature and often experience fluctuating demands across different resources. In this paper, we propose a dynamic resource reconfiguration strategy called VMatch that generates an efficient relocation/remapping plan for already assigned virtual links (VLs) facing bandwidth expansion. The overall problem is formulated as a one-to-one matching game that aims to minimize the relocation cost from the users perspective and at the same time improves resource utilization from a service providers (SPs) perspective. By using the concept of preferences in the matching game, different stakeholders, i.e., end-users and SPs express their priorities. Thorough simulation analysis of the proposed approach shows that the model on an average can reduce the remapping cost by 19% and improve server utilization by 21% in comparison with the baselines.

This paper proposes a virtual machine (VM) migration model to reduce the power consumption while migrating a set of VMs over geo-distributed clouds. We develop an approach to find out the migration path across different Internet Service Providers (ISPs) leading to the most feasible destination. For this, we make use of the variation in the electricity price at the ISPs for deciding the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. Hence, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between the power consumption and the migration time to make the implementation realistic. Thorough simulation analysis of the proposed approach shows that it can achieve low power consumption cost with acceptable migration time.